Hitherto, images have been used for a variety of verifications. For example, a lesion and the like have been confirmed using images regarding a human's features. In addition, the presence or absence of foreign matter mixed during the manufacture of a product and a defect have also been confirmed using an image of the product. Further, a defect of a semiconductor part disposed on a semiconductor substrate has also been confirmed using an image of the manufactured semiconductor substrate. When such confirmation is performed, a machine learning technique called “deep learning” has also been used as auxiliary means therefor to avoid oversight.
As a method using machine learning, a method using a classifier caused to learn using learning data is known. For example, when a “lesion” being a change in mind and body which is generated due to a disease is verified from image data by using machine learning, a plurality of pieces of image data of a “positive example” having a target lesion and a plurality of pieces of image data of a “negative example” having no target lesion are prepared and set to be learning data.
At that time, the accuracy of the classifier depends on learning data. However, the accuracy does not necessarily become higher as the number of pieces of learning data increases. For example, an increase in the number of pieces of data may results in a state called “over-learning” in which the accuracy in another data decreases due to excessive application to given data. When the accuracy of the classifier is improved by avoiding the over-learning, a variety of tunings are required.
Examples of the related art include Japanese Unexamined Patent Application Publication No. 2015-176283, Japanese Unexamined Patent Application Publication No. 2015-176169, Japanese Unexamined Patent Application Publication No. 2014-135092, Japanese Unexamined Patent Application Publication No. 2005-115525, and https://blogs.nvidia.com/blog/2016/05/10/deep-learning-chest-x-rays/ (searched on Jul. 22, 2016).
As described above, in a case where a classifier is used, a variety of adjustments such as tuning are required, and thus it is difficult to easily obtain a highly accurate result. Therefore, for example, it is difficult for a user or the like who is not familiar with machine learning to easily obtain a highly accurate result by using machine learning.
It could therefore be helpful to provide a verification device capable of easily obtaining a highly accurate verification result by using machine learning.